1 TADPOLE Univariate

1.0.1 Loading the libraries

library("FRESA.CAD")
library(psych)
library(whitening)
library("vioplot")

library(readxl)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 The data set

TADPOLE_D1_D2 <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2.csv")
TADPOLE_D1_D2_Dict <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict.csv")
TADPOLE_D1_D2_Dict_LR <- as.data.frame(read_excel("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict_LR.xlsx",sheet = "LeftRightFeatures"))


rownames(TADPOLE_D1_D2_Dict) <- TADPOLE_D1_D2_Dict$FLDNAME

1.2 Conditioning the data


# mm3 to mm
isVolume <- c("Ventricles","Hippocampus","WholeBrain","Entorhinal","Fusiform","MidTemp","ICV",
              TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Volume")]
              )


#TADPOLE_D1_D2[,isVolume] <- apply(TADPOLE_D1_D2[,isVolume],2,'^',(1/3))
TADPOLE_D1_D2[,isVolume] <- TADPOLE_D1_D2[,isVolume]^(1/3)

# mm2 to mm
isArea <- TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Area")]
TADPOLE_D1_D2[,isArea] <- sqrt(TADPOLE_D1_D2[,isArea])

# Get only cross sectional measurements
FreeSurfersetCross <- str_detect(colnames(TADPOLE_D1_D2),"UCSFFSX")

# The subset of baseline measurements
baselineTadpole <- subset(TADPOLE_D1_D2,VISCODE=="bl")
table(baselineTadpole$DX)

                   Dementia Dementia to MCI             MCI MCI to Dementia 
          7             336               1             864               5 
  MCI to NL              NL       NL to MCI 
          2             521               1

table(baselineTadpole$DX_bl)

AD CN EMCI LMCI SMC 342 417 310 562 106


rownames(baselineTadpole) <- baselineTadpole$PTID


validBaselineTadpole <- cbind(DX=baselineTadpole$DX_bl,
                                 AGE=baselineTadpole$AGE,
                                 Gender=1*(baselineTadpole$PTGENDER=="Female"),
                                 ADAS11=baselineTadpole$ADAS11,
                                 ADAS13=baselineTadpole$ADAS13,
                                 MMSE=baselineTadpole$MMSE,
                                 RAVLT_immediate=baselineTadpole$RAVLT_immediate,
                                 RAVLT_learning=baselineTadpole$RAVLT_learning,
                                 RAVLT_forgetting=baselineTadpole$RAVLT_forgetting,
                                 RAVLT_perc_forgetting=baselineTadpole$RAVLT_perc_forgetting,
                                 FAQ=baselineTadpole$FAQ,
                                 Ventricles=baselineTadpole$Ventricles,
                                 Hippocampus=baselineTadpole$Hippocampus,
                                 WholeBrain=baselineTadpole$WholeBrain,
                                 Entorhinal=baselineTadpole$Entorhinal,
                                 Fusiform=baselineTadpole$Fusiform,
                                 MidTemp=baselineTadpole$MidTemp,
                                 ICV=baselineTadpole$ICV,
                                 baselineTadpole[,FreeSurfersetCross])


LeftFields <- TADPOLE_D1_D2_Dict_LR$LFN
names(LeftFields) <- LeftFields
LeftFields <- LeftFields[LeftFields %in% colnames(validBaselineTadpole)]
RightFields <- TADPOLE_D1_D2_Dict_LR$RFN
names(RightFields) <- RightFields
RightFields <- RightFields[RightFields %in% colnames(validBaselineTadpole)]

## Normalize to ICV
validBaselineTadpole$Ventricles=validBaselineTadpole$Ventricles/validBaselineTadpole$ICV
validBaselineTadpole$Hippocampus=validBaselineTadpole$Hippocampus/validBaselineTadpole$ICV
validBaselineTadpole$WholeBrain=validBaselineTadpole$WholeBrain/validBaselineTadpole$ICV
validBaselineTadpole$Entorhinal=validBaselineTadpole$Entorhinal/validBaselineTadpole$ICV
validBaselineTadpole$Fusiform=validBaselineTadpole$Fusiform/validBaselineTadpole$ICV
validBaselineTadpole$MidTemp=validBaselineTadpole$MidTemp/validBaselineTadpole$ICV

leftData <- validBaselineTadpole[,LeftFields]/validBaselineTadpole$ICV
RightData <- validBaselineTadpole[,RightFields]/validBaselineTadpole$ICV

## get mean and relative difference 
meanLeftRight <- (leftData + RightData)/2
difLeftRight <- abs(leftData - RightData)
reldifLeftRight <- difLeftRight/meanLeftRight
colnames(meanLeftRight) <- paste("M",colnames(meanLeftRight),sep="_")
colnames(difLeftRight) <- paste("D",colnames(difLeftRight),sep="_")
colnames(reldifLeftRight) <- paste("RD",colnames(reldifLeftRight),sep="_")


validBaselineTadpole <- validBaselineTadpole[,!(colnames(validBaselineTadpole) %in% 
                                               c(LeftFields,RightFields))]
#validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight,reldifLeftRight)
validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight)

## Remove columns with too many NA more than %15 of NA
nacount <- apply(is.na(validBaselineTadpole),2,sum)/nrow(validBaselineTadpole) < 0.15
diagnose <- validBaselineTadpole$DX
pander::pander(table(diagnose))

AD	CN	EMCI	LMCI	SMC
342	417	310	562	106

validBaselineTadpole <- validBaselineTadpole[,nacount]
## Remove character columns
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole <- validBaselineTadpole[,!ischar]
## Place back diagnose
validBaselineTadpole$DX <- diagnose


validBaselineTadpole <- validBaselineTadpole[complete.cases(validBaselineTadpole),]
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole[,!ischar] <- sapply(validBaselineTadpole[,!ischar],as.numeric)

colnames(validBaselineTadpole) <- str_remove_all(colnames(validBaselineTadpole),"_UCSFFSX_11_02_15_UCSFFSX51_08_01_16")
colnames(validBaselineTadpole) <- str_replace_all(colnames(validBaselineTadpole)," ","_")
validBaselineTadpole$LONISID <- NULL
validBaselineTadpole$IMAGEUID <- NULL
validBaselineTadpole$LONIUID <- NULL

diagnose <- as.character(validBaselineTadpole$DX)
validBaselineTadpole$DX <- diagnose
pander::pander(table(validBaselineTadpole$DX))

AD	CN	EMCI	LMCI	SMC
245	359	272	444	93



validBaselineTadpole[validBaselineTadpole$DX %in% c("EMCI","LMCI"),"DX"] <- "MCI" 
validBaselineTadpole[validBaselineTadpole$DX %in% c("CN","SMC"),"DX"] <- "NL" 

pander::pander(table(validBaselineTadpole$DX))

AD	MCI	NL
245	716	452

1.3 Get the Time To Event on MCI Subjects


subjectsID <- rownames(validBaselineTadpole)
visitsID <- unique(TADPOLE_D1_D2$VISCODE)
baseDx <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE=="bl",c("PTID","DX","EXAMDATE")]
rownames(baseDx) <- baseDx$PTID 
baseDx <- baseDx[subjectsID,]
lastDx <- baseDx
toDementia <- baseDx
table(lastDx$DX)

   Dementia Dementia to MCI             MCI MCI to Dementia       MCI to NL 
        244               1             711               2               2 
         NL       NL to MCI 
        452               1

hasDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]


for (vid in visitsID)
{
  DxValue <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE==vid,c("PTID","DX","EXAMDATE")]
  rownames(DxValue) <- DxValue$PTID 
  DxValue <- DxValue[DxValue$PTID %in% subjectsID,]
  noDX <- DxValue$PTID[nchar(DxValue$DX) < 1]
  print(length(noDX))
  DxValue[noDX,] <- lastDx[noDX,]
  inLast <- lastDx$PTID[lastDx$PTID %in% DxValue$PTID]
  print(length(inLast))
  lastDx[inLast,] <- DxValue[inLast,]
  noDementia <- !(toDementia$PTID %in% hasDementia)
  toDementia[noDementia,] <- lastDx[noDementia,]
  hasDementia <- unique(c(hasDementia,lastDx$PTID[str_detect(lastDx$DX,"Dementia")]))
}

[1] 0 [1] 1413 [1] 2 [1] 1326 [1] 6 [1] 1218 [1] 23 [1] 1095 [1] 805 [1] 1058 [1] 29 [1] 710 [1] 20 [1] 212 [1] 14 [1] 167 [1] 32 [1] 553 [1] 25 [1] 298 [1] 18 [1] 130 [1] 667 [1] 667 [1] 112 [1] 112 [1] 176 [1] 176 [1] 177 [1] 177 [1] 625 [1] 625 [1] 251 [1] 251 [1] 159 [1] 159 [1] 7 [1] 7 [1] 17 [1] 99 [1] 9 [1] 63 [1] 1 [1] 1

table(lastDx$DX)

   Dementia Dementia to MCI             MCI MCI to Dementia       MCI to NL 
        428               2             463              80               7 
         NL  NL to Dementia       NL to MCI 
        406               1              26

baseMCI <-baseDx$PTID[baseDx$DX == "MCI"]
lastDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
lastDementia2 <- toDementia$PTID[str_detect(toDementia$DX,"Dementia")]
lastNL <- lastDx$PTID[str_detect(lastDx$DX,"NL")]

MCIatBaseline <- baseDx[baseMCI,]
MCIatEvent <- toDementia[baseMCI,]
MCIatLast <- lastDx[baseMCI,]

MCIconverters <- MCIatBaseline[baseMCI %in% lastDementia,]
MCI_No_converters <- MCIatBaseline[!(baseMCI %in% MCIconverters$PTID),]
MCIconverters$TimeToEvent <- (as.Date(toDementia[MCIconverters$PTID,"EXAMDATE"]) 
                                   - as.Date(MCIconverters$EXAMDATE))

sum(MCIconverters$TimeToEvent ==0)

[1] 0



MCIconverters$AtEventDX <- MCIatEvent[MCIconverters$PTID,"DX"]
MCIconverters$LastDX <- MCIatLast[MCIconverters$PTID,"DX"]

MCI_No_converters$TimeToEvent <- (as.Date(lastDx[MCI_No_converters$PTID,"EXAMDATE"]) 
                                   - as.Date(MCI_No_converters$EXAMDATE))

MCI_No_converters$LastDX <- MCIatLast[MCI_No_converters$PTID,"DX"]

MCI_No_converters <- subset(MCI_No_converters,TimeToEvent > 0)

2 Prognosis MCI to AD Conversion

2.1 the set


MCIPrognosisIDs <- c(MCIconverters$PTID,MCI_No_converters$PTID)

TADPOLECrossMRI <- validBaselineTadpole[MCIPrognosisIDs,]
table(TADPOLECrossMRI$DX)

MCI 680

TADPOLECrossMRI$DX <- NULL
TADPOLECrossMRI$status <- 1*(rownames(TADPOLECrossMRI) %in% MCIconverters$PTID)
table(TADPOLECrossMRI$status)

0 1 436 244

2.1.0.1 Standarize the names for the reporting

dataframe <- TADPOLECrossMRI
outcome <- "status"

trainFraction <- 0.5
rhoThreshold <- 0.6
TopVariables <- 5
aucTHR <- 0.55

set.seed(5)
trainSample <- sample(nrow(dataframe),nrow(dataframe)*trainFraction)

trainDataFrame <- dataframe[trainSample,]
testDataFrame <- dataframe[-trainSample,]

2.1.1 Data specs

pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))

rows	col
680	327

pander::pander(table(dataframe[,outcome]))

0	1
436	244

pander::pander(table(trainDataFrame[,outcome]))

0	1
208	132

pander::pander(table(testDataFrame[,outcome]))

0	1
228	112


varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]

2.2 Univariate


univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","IDI","ROCAUC","cStatCorr")
univar <- uniRankVar(varlist,
               paste(outcome,"~1"),
               outcome,
               trainDataFrame,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : D_ST47CV



#univar$orderframe[1:5,univariate_columns]
univarTest <- uniRankVar(varlist,
               paste(outcome,"~1"),
               outcome,
               trainDataFrame,
               testData=testDataFrame,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : D_ST47CV


univar$orderframe$BACC <- (univar$orderframe$Sensitivity + univar$orderframe$Specificity)/2.0
univarTest$orderframe$BACC <- (univarTest$orderframe$Sensitivity + univarTest$orderframe$Specificity)/2.0

#pROC::roc(trainDataFrame$class,trainDataFrame[,univar$orderframe$Name[1]],direction=">",plot=TRUE,auc=TRUE,quiet = TRUE)

2.3 Decorrelation with UPSTM Blind

DEdataframe <- IDeA(trainDataFrame,thr=rhoThreshold)
predTestDe <- predictDecorrelate(DEdataframe,testDataFrame)

ltvar <- getLatentCoefficients(DEdataframe);
pander::pander(head(ltvar))

La_ADAS13:

ADAS11 ADAS13

-1.39 1
La_RAVLT_immediate:

ADAS11 RAVLT_immediate

1.38 1
La_RAVLT_perc_forgetting:

RAVLT_immediate RAVLT_forgetting RAVLT_perc_forgetting

1.58 -10.1 1
La_WholeBrain:

WholeBrain M_ST36CV

1 -2.7
La_ST10CV:

ICV ST10CV

-0.998 1
La_ST127SV:

Ventricles ST127SV

-23.2 1

ADAS11	ADAS13
-1.39	1

ADAS11	RAVLT_immediate
1.38	1

RAVLT_immediate	RAVLT_forgetting	RAVLT_perc_forgetting
1.58	-10.1	1

WholeBrain	M_ST36CV
1	-2.7

ICV	ST10CV
-0.998	1

Ventricles	ST127SV
-23.2	1

pander::pander(c(Avlen=mean(sapply(ltvar,length))))

Avlen
2.41

pander::pander(c(Latent=length(ltvar)))

Latent
119



varlistDe <-  colnames(DEdataframe)[colnames(DEdataframe) != outcome];
univarDe <- uniRankVar(varlistDe,
              paste(outcome,"~1"),
                outcome,
              DEdataframe,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeTest <- uniRankVar(varlistDe,
              paste(outcome,"~1"),
                outcome,
              DEdataframe,
              testData=predTestDe,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDe$orderframe$BACC <- (univarDe$orderframe$Sensitivity + univarDe$orderframe$Specificity)/2.0
univarDeTest$orderframe$BACC <- (univarDeTest$orderframe$Sensitivity + univarDeTest$orderframe$Specificity)/2.0

#univarDe$orderframe[1:5,univariate_columns]
#univarDeTest$orderframe[1:5,univariate_columns]

#pROC::roc(DEdataframe$class,DEdataframe[,univarDe$orderframe$Name[1]],direction=">",plot=TRUE,auc=TRUE,quiet = TRUE)

2.4 Decorrelation with UPSTM Blind/Spearman

DEdataframeSpear <- IDeA(trainDataFrame,thr=rhoThreshold,method="spearman")
predTestDeSpear <- predictDecorrelate(DEdataframeSpear,testDataFrame)

ltvar <- getLatentCoefficients(DEdataframeSpear);
pander::pander(head(ltvar))

La_ADAS13:

ADAS11 ADAS13

-1.39 1
La_RAVLT_immediate:

ADAS11 RAVLT_immediate

1.38 1
La_RAVLT_perc_forgetting:

RAVLT_immediate RAVLT_forgetting RAVLT_perc_forgetting

1.58 -10.1 1
La_Hippocampus:

Hippocampus M_ST24TA

1 -1.62
La_WholeBrain:

WholeBrain M_ST31TA

1 -7.57
La_ST10CV:

ICV ST10CV

-0.998 1

ADAS11	ADAS13
-1.39	1

ADAS11	RAVLT_immediate
1.38	1

RAVLT_immediate	RAVLT_forgetting	RAVLT_perc_forgetting
1.58	-10.1	1

Hippocampus	M_ST24TA
1	-1.62

WholeBrain	M_ST31TA
1	-7.57

ICV	ST10CV
-0.998	1

pander::pander(c(Avlen=mean(sapply(ltvar,length))))

Avlen
2.35

pander::pander(c(Latent=length(ltvar)))

Latent
111



varlistDeSpear <-  colnames(DEdataframeSpear)[colnames(DEdataframeSpear) != outcome];
univarDeSpear <- uniRankVar(varlistDeSpear,
              paste(outcome,"~1"),
                outcome,
              DEdataframeSpear,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeSpearTest <- uniRankVar(varlistDeSpear,
              paste(outcome,"~1"),
                outcome,
              DEdataframeSpear,
              testData=predTestDeSpear,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeSpear$orderframe$BACC <- (univarDeSpear$orderframe$Sensitivity + univarDeSpear$orderframe$Specificity)/2.0
univarDeSpearTest$orderframe$BACC <- (univarDeSpearTest$orderframe$Sensitivity + univarDeSpearTest$orderframe$Specificity)/2.0

2.5 Decorrelation with UPSTM Driven


DriDEdataframe <- IDeA(trainDataFrame,Outcome=outcome,thr=rhoThreshold)
predTestDri <- predictDecorrelate(DriDEdataframe,testDataFrame)


ltvar <- getLatentCoefficients(DriDEdataframe);
pander::pander(head(ltvar))

La_ADAS11:

ADAS11 ADAS13

1 -0.649
La_RAVLT_immediate:

ADAS13 RAVLT_immediate

1.02 1
La_RAVLT_forgetting:

RAVLT_learning RAVLT_forgetting RAVLT_perc_forgetting

-0.373 1 -0.0585
La_Ventricles:

Ventricles ST7SV

1 -0.0322
La_Hippocampus:

Hippocampus M_ST29SV

1 -1.23
La_WholeBrain:

WholeBrain M_ST36CV

1 -2.7

ADAS11	ADAS13
1	-0.649

ADAS13	RAVLT_immediate
1.02	1

RAVLT_learning	RAVLT_forgetting	RAVLT_perc_forgetting
-0.373	1	-0.0585

Ventricles	ST7SV
1	-0.0322

Hippocampus	M_ST29SV
1	-1.23

WholeBrain	M_ST36CV
1	-2.7

pander::pander(c(Avlen=mean(sapply(ltvar,length))))

Avlen
2.4

pander::pander(c(Latent=length(ltvar)))

Latent
117



varlistDe <-  colnames(DriDEdataframe)[colnames(DriDEdataframe) != outcome];
univarDeDri <- uniRankVar(varlistDe,
              paste(outcome,"~1"),
                outcome,
              DriDEdataframe,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeDriTest <- uniRankVar(varlistDe,
              paste(outcome,"~1"),
                outcome,
              DriDEdataframe,
              testData=predTestDri,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeDri$orderframe$BACC <- (univarDeDri$orderframe$Sensitivity + univarDeDri$orderframe$Specificity)/2.0
univarDeDriTest$orderframe$BACC <- (univarDeDriTest$orderframe$Sensitivity + univarDeDriTest$orderframe$Specificity)/2.0

2.6 Decorrelation with UPSTM Driven and Spearman


DriDEdataframeSpear <- IDeA(trainDataFrame,Outcome=outcome,thr=rhoThreshold,method="spearman")
predTestDriSpear <- predictDecorrelate(DriDEdataframeSpear,testDataFrame)


ltvar <- getLatentCoefficients(DriDEdataframeSpear);
pander::pander(head(ltvar))

La_ADAS11:

ADAS11 ADAS13

1 -0.649
La_RAVLT_immediate:

ADAS13 RAVLT_immediate

1.02 1
La_RAVLT_forgetting:

RAVLT_learning RAVLT_forgetting RAVLT_perc_forgetting

-0.373 1 -0.0585
La_Ventricles:

Ventricles ST7SV

1 -0.0322
La_Hippocampus:

Hippocampus M_ST29SV

1 -1.23
La_WholeBrain:

WholeBrain M_ST29SV

1 -1.94

ADAS11	ADAS13
1	-0.649

ADAS13	RAVLT_immediate
1.02	1

RAVLT_learning	RAVLT_forgetting	RAVLT_perc_forgetting
-0.373	1	-0.0585

Ventricles	ST7SV
1	-0.0322

Hippocampus	M_ST29SV
1	-1.23

WholeBrain	M_ST29SV
1	-1.94

pander::pander(c(Avlen=mean(sapply(ltvar,length))))

Avlen
2.28

pander::pander(c(Latent=length(ltvar)))

Latent
105



varlistDeSpear <-  colnames(DriDEdataframeSpear)[colnames(DriDEdataframeSpear) != outcome];
univarDeDriSpear <- uniRankVar(varlistDeSpear,
              paste(outcome,"~1"),
                outcome,
              DriDEdataframeSpear,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeDriSpearTest <- uniRankVar(varlistDeSpear,
              paste(outcome,"~1"),
                outcome,
              DriDEdataframeSpear,
              testData=predTestDriSpear,
              rankingTest = "CStat")

100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV


univarDeDriSpear$orderframe$BACC <- (univarDeDriSpear$orderframe$Sensitivity + univarDeDriSpear$orderframe$Specificity)/2.0
univarDeDriSpearTest$orderframe$BACC <- (univarDeDriSpearTest$orderframe$Sensitivity + univarDeDriSpearTest$orderframe$Specificity)/2.0

2.6.1 Get continous correlated features

iscontinous <- sapply(apply(trainDataFrame,2,unique),length) > 5 ## Only variables with enough samples

noclassData <- trainDataFrame[,iscontinous]
cmat <- cor(noclassData);
diag(cmat) <- 0;
maxcor <- apply(cmat>rhoThreshold,2,sum);
topcor <- names(maxcor[maxcor > 0]) ## Only correlated features will be PCA
pander::pander(c(Ncor=length(topcor)))

Ncor
192

cmat <- NULL

notcorr <- colnames(trainDataFrame)[!(colnames(trainDataFrame) %in% topcor)]
noclassData <- noclassData[,topcor]
noclassTestData <- testDataFrame[,topcor]

2.7 PCA Analysis


### PCA 

pc <- principal(noclassData,4*TopVariables,rotate="varimax")   #principal components
pander::pander(t(pc$loadings[1:TopVariables,1:TopVariables]))

	ADAS11	ADAS13	RAVLT_immediate	RAVLT_learning	RAVLT_forgetting
RC1	-0.2472	-0.27699	0.3309	0.2521	0.0109
RC2	0.0964	0.14173	-0.2176	-0.2052	-0.0339
RC4	-0.0679	-0.07057	0.0798	-0.0374	-0.0427
RC6	-0.0259	-0.00945	0.1057	-0.0340	-0.0777
RC5	-0.7110	-0.75567	0.5859	0.5115	-0.3904

PCA_Train <- as.data.frame(cbind(predict(pc,noclassData),trainDataFrame[,notcorr]))
colnames(PCA_Train) <- c(colnames(predict(pc,noclassData)),notcorr)

PCA_Predicted <- as.data.frame(cbind(predict(pc,noclassTestData),testDataFrame[,notcorr]))
colnames(PCA_Predicted) <- c(colnames(predict(pc,noclassTestData)),notcorr)

iscontinous <- colnames(PCA_Predicted)[sapply(apply(PCA_Predicted,2,unique),length) > 5] ## Only variables with enough samples
varlistPCA <-  iscontinous;

univarPCA <- uniRankVar(varlistPCA,
              paste(outcome,"~1"),
                outcome,
              PCA_Train,
              rankingTest = "CStat")

100 : D_ST31TS


univarPCATest <- uniRankVar(varlistPCA,
              paste(outcome,"~1"),
                outcome,
              PCA_Train,
              testData=PCA_Predicted,
              rankingTest = "CStat")

100 : D_ST31TS


univarPCA$orderframe$BACC <- (univarPCA$orderframe$Sensitivity + univarPCA$orderframe$Specificity)/2.0
univarPCATest$orderframe$BACC <- (univarPCATest$orderframe$Sensitivity + univarPCATest$orderframe$Specificity)/2.0

2.8 EFA


uls <- fa(noclassData,4*TopVariables,rotate="varimax")  #unweighted least squares is minres 
pander::pander(t(uls$weights[1:TopVariables,1:TopVariables]))

	ADAS11	ADAS13	RAVLT_immediate	RAVLT_learning	RAVLT_forgetting
MR1	0.0583	-0.0786	0.000637	0.00973	-0.044826
MR2	-0.0596	0.0695	-0.035720	0.01209	0.000487
MR4	0.0725	-0.0852	-0.153868	0.05421	-0.039901
MR3	-0.0643	0.2033	0.022566	-0.02166	0.089076
MR5	-0.1106	0.1013	-0.048827	0.03481	-0.032652

EFA_Train <- as.data.frame(cbind(predict(uls,noclassData),trainDataFrame[,notcorr]))
colnames(EFA_Train) <- c(colnames(predict(uls,noclassData)),notcorr)
EFA_Predicted <- as.data.frame(cbind(predict(uls,noclassTestData),testDataFrame[,notcorr]))
colnames(EFA_Predicted) <- c(colnames(predict(uls,noclassTestData)),notcorr)

iscontinous <- colnames(EFA_Predicted)[sapply(apply(EFA_Predicted,2,unique),length) > 5] ## Only variables with enough 
varlistEFA <-  iscontinous
univarEFA <- uniRankVar(varlistEFA,
              paste(outcome,"~1"),
                outcome,
              EFA_Train,
              rankingTest = "CStat")

100 : D_ST31TS


univarEFATest <- uniRankVar(varlistEFA,
              paste(outcome,"~1"),
                outcome,
              EFA_Train,
              testData=EFA_Predicted,
              rankingTest = "CStat")

100 : D_ST31TS


univarEFA$orderframe$BACC <- (univarEFA$orderframe$Sensitivity + univarEFA$orderframe$Specificity)/2.0
univarEFATest$orderframe$BACC <- (univarEFATest$orderframe$Sensitivity + univarEFATest$orderframe$Specificity)/2.0

2.9 White

WhiteMat = whiteningMatrix(cov(noclassData), method="PCA")
sum(is.na(WhiteMat))

[1] 0

tokeep <- apply(is.na(WhiteMat),1,sum) == 0
WhiteMat <- WhiteMat[tokeep,]
sum(is.na(WhiteMat))

[1] 0

sum(apply(abs(WhiteMat),1,sum) > 1.0e6)

[1] 0

tokeep <- apply(abs(WhiteMat),1,sum) < 1.0e6
WhiteMat <- WhiteMat[tokeep,]
sum(apply(abs(WhiteMat),1,sum) > 1.0e6)

[1] 0


pander::pander(c(ncol=ncol(WhiteMat),nrow=nrow(WhiteMat)))

ncol	nrow
192	192


pander::pander(WhiteMat[1:TopVariables,1:TopVariables])

	ADAS11	ADAS13	RAVLT_immediate	RAVLT_learning	RAVLT_forgetting
L1	0.00197	0.00336	-0.00552	-0.00107	0.001595
L2	0.03130	0.04447	-0.08537	-0.01298	-0.013590
L3	0.01492	0.02388	0.00350	-0.00265	0.000399
L4	0.09347	0.12706	0.10296	0.00277	0.005494
L5	-0.01022	-0.00902	0.01445	0.01523	0.004159

PCAWhite_Train <- as.data.frame(cbind(tcrossprod(as.matrix(noclassData), WhiteMat),trainDataFrame[,notcorr]))
colnames(PCAWhite_Train) <- c(colnames(tcrossprod(as.matrix(noclassData), WhiteMat)),notcorr)

sum(is.na(PCAWhite_Train))

[1] 0




PCAWhitePredicted <- as.data.frame(cbind(tcrossprod(as.matrix(noclassTestData), WhiteMat),testDataFrame[,notcorr]))
colnames(PCAWhitePredicted) <- c(colnames(tcrossprod(as.matrix(noclassTestData), WhiteMat)),notcorr)

sum(is.na(PCAWhitePredicted))

[1] 0


iscontinous <- colnames(PCAWhitePredicted)[sapply(apply(PCAWhitePredicted,2,unique),length) > 5] ## Only variables with enough 
varlistWhite <-  iscontinous

univarWhite <- uniRankVar(varlistWhite,
              paste(outcome,"~1"),
                outcome,
              PCAWhite_Train,
              rankingTest = "CStat")

100 : L100 200 : M_ST23TS 300 : D_ST129SA



univarWhiteTest <- uniRankVar(varlistWhite,
              paste(outcome,"~1"),
                outcome,
              PCAWhite_Train,
              testData=PCAWhitePredicted,
              rankingTest = "CStat")

100 : L100 200 : M_ST23TS 300 : D_ST129SA


univarWhite$orderframe$BACC <- (univarWhite$orderframe$Sensitivity + univarWhite$orderframe$Specificity)/2.0
univarWhiteTest$orderframe$BACC <- (univarWhiteTest$orderframe$Sensitivity + univarWhiteTest$orderframe$Specificity)/2.0

2.10 Correlation Matrices

2.10.1 RAW

par(cex=1.0,cex.main=0.8)
breaks <- c(0:5)/5.0;

cormat <- cor(testDataFrame,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(max(abs(cormat)))

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.294	0.433	0.535	0.627	0.792

pander::pander(c(Raw_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

Raw_fraction
0.0136


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Raw Correlation",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature"
                  )


#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Raw Correlation",xlab="Spearman Correlation")

rawDen <- density(cormat,from=-1,to=1)
par(op)

2.10.2 UPSTM Blind

par(cex=1.0,cex.main=0.8)

## Train Correlation

cormat <- cor(DEdataframe,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])

pander::pander(c(Train=max(abs(cormat))))

Train
0.599

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.144	0.2	0.265	0.353	0.536

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
0


## Test Correlation
cormat <- cor(predTestDe,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.853

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.145	0.202	0.269	0.359	0.538

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
0.00026


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after HCA",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")


#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after UPSTM",xlab="Spearman Correlation")

DeDen <- density(cormat,from=-1,to=1)


par(op)

2.10.3 UPSTM Blind/Spearman

par(cex=1.0,cex.main=0.8)

## Train Correlation

cormat <- cor(DEdataframeSpear,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])

pander::pander(c(Train=max(abs(cormat))))

Train
0.837

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.143	0.195	0.257	0.347	0.542

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
0.000335


## Test Correlation
cormat <- cor(predTestDeSpear,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.878

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.142	0.197	0.264	0.353	0.54

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
0.000335


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after HCA:Spearman",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")


#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after UPSTM",xlab="Spearman Correlation")

DeSpearDen <- density(cormat,from=-1,to=1)

par(op)

2.10.4 UPSTM Driven

par(cex=1.0,cex.main=0.8)

## Train Correlation

cormat <- cor(DriDEdataframe,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])

pander::pander(c(Train=max(abs(cormat))))

Train
0.599

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.15	0.209	0.276	0.369	0.537

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
0


## Test Correlation
cormat <- cor(DriDEdataframe,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.769

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.147	0.205	0.271	0.36	0.527

pander::pander(c(HCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCA_fraction
7.44e-05


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after Driven-HCA",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after Driven-UPSTM",xlab="Spearman Correlation")

DeDrivDen <- density(cormat,from=-1,to=1)
par(op)

2.10.5 UPSTM Spearman

par(cex=1.0,cex.main=0.8)

## Train Correlation

cormat <- cor(DriDEdataframeSpear,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])

pander::pander(c(Train=max(abs(cormat))))

Train
0.835

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.155	0.216	0.287	0.379	0.553

pander::pander(c(HCAS_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCAS_fraction
0.000316


## Test Correlation

cormat <- cor(predTestDriSpear,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.84

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.157	0.225	0.296	0.387	0.556

pander::pander(c(HCAS_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

HCAS_fraction
0.000446


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation: Driven/Spearman",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")


#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after UPSTM with Spearman",xlab="Spearman Correlation")

DeDrivSpearDen <- density(cormat,from=-1,to=1)
par(op)

2.10.6 PCA

par(cex=1.0,cex.main=0.8)



## Train Correlation

cormat <- cor(PCA_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Train=max(abs(cormat))))

Train
0.626

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.119	0.155	0.204	0.317	0.531

pander::pander(c(PCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

PCA_fraction
0.000164


## Test Correlation
cormat <- cor(PCA_Predicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.597

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.115	0.149	0.198	0.316	0.532

pander::pander(c(PCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

PCA_fraction
0


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after PCA",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")



#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after PCA",xlab="Spearman Correlation")

PCADen <- density(cormat,from=-1,to=1)

par(op)

2.10.7 EFA

par(cex=1.0,cex.main=0.8)

## Train Correlation

cormat <- cor(EFA_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Train=max(abs(cormat))))

Train
0.626

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.119	0.155	0.203	0.316	0.531

pander::pander(c(EFA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

EFA_fraction
8.22e-05


## Test Correlation
cormat <- cor(EFA_Predicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.597

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.117	0.153	0.205	0.314	0.526

pander::pander(c(EFA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

EFA_fraction
0


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after EFA",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")



#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after EFA",xlab="Spearman Correlation")

EFADen <- density(cormat,from=-1,to=1)
par(op)

2.10.8 PCA Whitening



## Train Correlation

cormat <- cor(PCAWhite_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Train=max(abs(cormat))))

Train
0.599

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.0851	0.108	0.133	0.171	0.395

pander::pander(c(PCAWhite_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

PCAWhite_fraction
0


## Test Correlation
cormat <- cor(PCAWhitePredicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;

pander::pander(c(Test=max(abs(cormat))))

Test
0.582

pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))

90%	95%	97.5%	99%	99.9%
0.115	0.14	0.166	0.204	0.384

pander::pander(c(PCAWhite_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))

PCAWhite_fraction
0


gplots::heatmap.2(abs(cormat),
                  trace = "none",
                  mar = c(5,5),
                  col=rev(heat.colors(5)),
                  breaks = breaks,
                  main = "Correlation after PCAWhite",
                  cexRow = 0.25,
                  cexCol = 0.25,
                  srtCol=35,
                  srtRow=75,
                  key.title=NA,
                  key.xlab="Spearman Correlation",
                  xlab="Feature", ylab="Feature")



#hist(cormat,freq=FALSE,
#     density=NULL,
#     xlim=c(-1,1),
#     ylim=c(0,4.0),
#     main="Correlation after PCAWhite",xlab="Spearman Correlation")

WhiteDen <- density(cormat,from=-1,to=1)
par(op)

2.11 The Density Plot

par(cex=0.7)
colors=c("red","blue","green","darkblue","darkgreen","purple","orange","darkred");

plot(rawDen,
     xlim=c(-1,1),
     ylim=c(0.001,7.0),
     col=colors[1],
     lty=1,
     lwd=4,
     log="y",
     main="Test: Correlation Distribution",xlab="Spearman Correlation")

lines(DeDen,col=colors[2],lty=2,lwd=4)
lines(DeSpearDen,col=colors[3],lty=3,lwd=4)
lines(DeDrivDen,col=colors[4],lty=4,lwd=2)
lines(DeDrivSpearDen,col=colors[5],lty=5,lwd=2)

lines(PCADen,col=colors[6],lty=6,lwd=1)
lines(EFADen,col=colors[7],lty=7,lwd=1)
lines(WhiteDen,col=colors[8],lty=8,lwd=1)

names=c("Raw","HCA:P","HCA:S","DHCA:P","DHCA:S","PCA","EFA","White:PCA")
#colors=c("red","blue","green","blue","green","purple","purple","gray");
lines=c(1,2,3,4,5,6,7,8)
lwds=c(4,4,4,2,2,1,1,1)

legend("topleft",names,col=colors,lty=lines,lwd=lwds,cex=0.50)

par(op)

2.11.1 Differences between train and test ROC AUC

par(op)
par(mfrow=c(1,1),cex=0.7)

AUCResults <- list();
diffAUC <- list();
thenames <- rownames(univar$orderframe)[(rownames(univar$orderframe) %in% colnames(testDataFrame))]
rawAUC <- univar$orderframe[thenames,"ROCAUC"]
thenames <- thenames[rawAUC >= aucTHR]
rawAUC <- univar$orderframe[thenames,"ROCAUC"]
rawAUCTest <- univarTest$orderframe[thenames,"ROCAUC"]
AUCResults$RAW <- rawAUCTest
diffAUC$RAW <-  rawAUCTest-rawAUC
plot(rawAUC,rawAUCTest-rawAUC,
     xlab="TRAIN ROC AUC",
     ylab="Test:AUC-Train:AUC",
     xlim=c(0.5,1.0),
     ylim=c(-0.25,0.25),
     pch=1,
     col=colors[1],
     main="ROC AUC Difference Between Test and Train")

thenames <- rownames(univarDe$orderframe)[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
HCAP <- univarDe$orderframe[thenames,"ROCAUC"]
thenames <- thenames[HCAP >= aucTHR]
HCAP <- univarDe$orderframe[thenames,"ROCAUC"]
HCAPTest <- univarDeTest$orderframe[thenames,"ROCAUC"]
AUCResults$HCAP <- HCAP
AUCResults$HCAP_T <- HCAPTest
diffAUC$HCAP <-  HCAPTest-HCAP

points(HCAP,HCAPTest-HCAP,pch=2,col=colors[2])

thenames <- rownames(univarDeSpear$orderframe)[!(rownames(univarDeSpear$orderframe) %in% colnames(testDataFrame))]
HCAS <- univarDeSpear$orderframe[thenames,"ROCAUC"]
thenames <- thenames[HCAS >= aucTHR]
HCAS <- univarDeSpear$orderframe[thenames,"ROCAUC"]
HCASTest <- univarDeSpearTest$orderframe[thenames,"ROCAUC"]
AUCResults$HCAS <- HCAS
AUCResults$HCAS_T <- HCASTest
diffAUC$HCAS <-  HCASTest-HCAS

points(HCAS,HCASTest-HCAS,pch=3,col=colors[3])

thenames <- rownames(univarDeDri$orderframe)[!(rownames(univarDeDri$orderframe) %in% colnames(testDataFrame))]
DHCAP <- univarDeDri$orderframe[thenames,"ROCAUC"]
thenames <- thenames[DHCAP >= aucTHR]
DHCAP <- univarDeDri$orderframe[thenames,"ROCAUC"]
DHCAPTest <- univarDeDriTest$orderframe[thenames,"ROCAUC"]
AUCResults$DHCAP <- DHCAP
AUCResults$DHCAP_T <- DHCAPTest
diffAUC$DHCAP <-  DHCAPTest-DHCAP

points(DHCAP,DHCAPTest-DHCAP,pch=4,col=colors[4])

thenames <- rownames(univarDeDriSpear$orderframe)[!(rownames(univarDeDriSpear$orderframe) %in% colnames(testDataFrame))]
DHCAS <- univarDeDriSpear$orderframe[thenames,"ROCAUC"]
thenames <- thenames[DHCAS >= aucTHR]
DHCAS <- univarDeDriSpear$orderframe[thenames,"ROCAUC"]
DHCASTest <- univarDeDriSpearTest$orderframe[thenames,"ROCAUC"]
AUCResults$DHCAS <- DHCAS
AUCResults$DHCAS_T <- DHCASTest
diffAUC$DHCAS <-  DHCASTest-DHCAS

points(DHCAS,DHCASTest-DHCAS,pch=5,col=colors[5])

thenames <- rownames(univarPCA$orderframe)[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCA <- univarPCA$orderframe[thenames,"ROCAUC"]
thenames <- thenames[PCA >= aucTHR]
PCA <- univarPCA$orderframe[thenames,"ROCAUC"]
PCATest <- univarPCATest$orderframe[thenames,"ROCAUC"]
AUCResults$PCA <- PCA
AUCResults$PCA_T <- PCATest
diffAUC$PCA <-  PCATest-PCA

points(PCA,PCATest-PCA,pch=6,col=colors[6])

thenames <- rownames(univarEFA$orderframe)[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFA <- univarEFA$orderframe[thenames,"ROCAUC"]
thenames <- thenames[EFA >= aucTHR]
EFA <- univarEFA$orderframe[thenames,"ROCAUC"]

EFATest <- univarEFATest$orderframe[thenames,"ROCAUC"]
AUCResults$EFA <- EFA
AUCResults$EFA_T <- EFATest
diffAUC$EFA <-  EFATest-EFA

points(EFA,EFATest-EFA,pch=7,col=colors[7])

thenames <- rownames(univarWhite$orderframe)[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCA <- univarWhite$orderframe[thenames,"ROCAUC"]
thenames <- thenames[WPCA >= aucTHR]
WPCA <- univarWhite$orderframe[thenames,"ROCAUC"]
WPCATest <- univarWhiteTest$orderframe[thenames,"ROCAUC"]
AUCResults$WPCA <- WPCA
AUCResults$WPCA_T <- WPCATest
diffAUC$WPCA <-  WPCATest-WPCA


points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])


names=c("Raw","HCA:P","HCA:S","DHCA:P","DHCA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)

legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)

2.11.2 Violin of differences


par(op)
par(mfrow=c(1,1),cex=0.7)

vioplot(diffAUC,
        ylim=c(-0.25,0.25),
        ylab="Test-Train",
        main="Test-Train Paired ROC AUC Difference",
        col=colors,
        cex.axis=0.6,
        las=2
)
stripchart(diffAUC, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffAUC),lapply(diffAUC,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.3 Distribution of ROC AUC in latent Variables

par(op)
par(mfrow=c(1,1),cex=0.7)

colors2 <- length(AUCResults)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(AUCResults,
        ylim=c(0.3,1.0),
        ylab="ROC AUC",
        main="ROC AUC of Latent Variables",
        col=colors2,
        cex.axis=0.6,
        las=2
)
abline(h=0.5,col="black")
stripchart(AUCResults, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(AUCResults),lapply(AUCResults,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.4 Differences between train and test Balanced Accuracy

par(op)
par(mfrow=c(1,1),cex=0.7)
BACCTHR <- aucTHR
BACCResults <- list();
diffBACC <- list();
thenames <- rownames(univar$orderframe)[(rownames(univar$orderframe) %in% colnames(testDataFrame))]
rawBACC <- univar$orderframe[thenames,"BACC"]
thenames <- thenames[rawBACC >= BACCTHR]
rawBACC <- univar$orderframe[thenames,"BACC"]
rawBACCTest <- univarTest$orderframe[thenames,"BACC"]
BACCResults$RAW <- rawBACCTest
diffBACC$RAW <-  rawBACCTest-rawBACC
plot(rawBACC,rawBACCTest-rawBACC,
     xlab="TRAIN Balanced Acc",
     ylab="Test:BACC-Train:BACC",
     xlim=c(0.5,1.0),
     ylim=c(-0.25,0.25),
     pch=1,
     col=colors[1],
     main="Balanced Acc Difference Between Test and Train")

thenames <- rownames(univarDe$orderframe)[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
HCAP <- univarDe$orderframe[thenames,"BACC"]
thenames <- thenames[HCAP >= BACCTHR]
HCAP <- univarDe$orderframe[thenames,"BACC"]
HCAPTest <- univarDeTest$orderframe[thenames,"BACC"]
BACCResults$HCAP <- HCAP
BACCResults$HCAP_T <- HCAPTest
diffBACC$HCAP <-  HCAPTest-HCAP

points(HCAP,HCAPTest-HCAP,pch=2,col=colors[2])

thenames <- rownames(univarDeSpear$orderframe)[!(rownames(univarDeSpear$orderframe) %in% colnames(testDataFrame))]
HCAS <- univarDeSpear$orderframe[thenames,"BACC"]
thenames <- thenames[HCAS >= BACCTHR]
HCAS <- univarDeSpear$orderframe[thenames,"BACC"]
HCASTest <- univarDeSpearTest$orderframe[thenames,"BACC"]
BACCResults$HCAS <- HCAS
BACCResults$HCAS_T <- HCASTest
diffBACC$HCAS <-  HCASTest-HCAS

points(HCAS,HCASTest-HCAS,pch=3,col=colors[3])

thenames <- rownames(univarDeDri$orderframe)[!(rownames(univarDeDri$orderframe) %in% colnames(testDataFrame))]
DHCAP <- univarDeDri$orderframe[thenames,"BACC"]
thenames <- thenames[DHCAP >= BACCTHR]
DHCAP <- univarDeDri$orderframe[thenames,"BACC"]
DHCAPTest <- univarDeDriTest$orderframe[thenames,"BACC"]
BACCResults$DHCAP <- DHCAP
BACCResults$DHCAP_T <- DHCAPTest
diffBACC$DHCAP <-  DHCAPTest-DHCAP

points(DHCAP,DHCAPTest-DHCAP,pch=4,col=colors[4])

thenames <- rownames(univarDeDriSpear$orderframe)[!(rownames(univarDeDriSpear$orderframe) %in% colnames(testDataFrame))]
DHCAS <- univarDeDriSpear$orderframe[thenames,"BACC"]
thenames <- thenames[DHCAS >= BACCTHR]
DHCAS <- univarDeDriSpear$orderframe[thenames,"BACC"]
DHCASTest <- univarDeDriSpearTest$orderframe[thenames,"BACC"]
BACCResults$DHCAS <- DHCAS
BACCResults$DHCAS_T <- DHCASTest
diffBACC$DHCAS <-  DHCASTest-DHCAS

points(DHCAS,DHCASTest-DHCAS,pch=5,col=colors[5])

thenames <- rownames(univarPCA$orderframe)[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCA <- univarPCA$orderframe[thenames,"BACC"]
thenames <- thenames[PCA >= BACCTHR]
PCA <- univarPCA$orderframe[thenames,"BACC"]
PCATest <- univarPCATest$orderframe[thenames,"BACC"]
BACCResults$PCA <- PCA
BACCResults$PCA_T <- PCATest
diffBACC$PCA <-  PCATest-PCA

points(PCA,PCATest-PCA,pch=6,col=colors[6])

thenames <- rownames(univarEFA$orderframe)[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFA <- univarEFA$orderframe[thenames,"BACC"]
thenames <- thenames[EFA >= BACCTHR]
EFA <- univarEFA$orderframe[thenames,"BACC"]

EFATest <- univarEFATest$orderframe[thenames,"BACC"]
BACCResults$EFA <- EFA
BACCResults$EFA_T <- EFATest
diffBACC$EFA <-  EFATest-EFA

points(EFA,EFATest-EFA,pch=7,col=colors[7])

thenames <- rownames(univarWhite$orderframe)[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCA <- univarWhite$orderframe[thenames,"BACC"]
thenames <- thenames[WPCA >= BACCTHR]
WPCA <- univarWhite$orderframe[thenames,"BACC"]
WPCATest <- univarWhiteTest$orderframe[thenames,"BACC"]
BACCResults$WPCA <- WPCA
BACCResults$WPCA_T <- WPCATest
diffBACC$WPCA <-  WPCATest-WPCA


points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])


names=c("Raw","HCA:P","HCA:S","DHCA:P","DHCA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)

legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)

2.11.5 Violin of differences


par(op)
par(mfrow=c(1,1),cex=0.7)

vioplot(diffBACC,
        ylim=c(-0.25,0.25),
        ylab="Test-Train",
        main="Test-Train Paired Balanced Acc Difference",
        col=colors,
        cex.axis=0.6,
        las=2
)
stripchart(diffBACC, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffBACC),lapply(diffBACC,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.6 Distribution of Balanced Acc in latent Variables

par(op)
par(mfrow=c(1,1),cex=0.7)

colors2 <- length(BACCResults)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(BACCResults,
        ylim=c(0.3,1.0),
        ylab="Balanced Acc",
        main="Balanced Acc of Latent Variables",
        col=colors2,
        cex.axis=0.6,
        las=2
)
abline(h=0.5,col="black")
stripchart(BACCResults, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(BACCResults),lapply(BACCResults,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.7 Differences between train and test IDI

par(op)
par(mfrow=c(1,1),cex=0.7)

testIDI <- list();
diffIDI <- list();
rawIDI <- univar$orderframe$IDI
rawIDITest <- univarTest$orderframe$IDI
testIDI$RAW <- rawIDITest
diffIDI$RAW <-  rawIDITest-rawIDI
plot(rawIDI,rawIDITest-rawIDI,
     xlab="TRAIN Test IDI",
     ylab="Test:IDI-Train:IDI",
     xlim=c(0,0.5),
     ylim=c(-0.2,0.2),
     pch=1,
     col=colors[1],
     main="Predict IDI Difference Between Test and Train")

HCAP <- univarDe$orderframe$IDI[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
HCAPTest <-univarDeTest$orderframe$IDI[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
testIDI$HCAP <- HCAP
testIDI$HCAP_T <- HCAPTest
diffIDI$HCAP <-  HCAPTest-HCAP

points(HCAP,HCAPTest-HCAP,pch=2,col=colors[2])

HCAS <- univarDeSpear$orderframe$IDI[!(rownames(univarDeSpearTest$orderframe) %in% colnames(testDataFrame))]
HCASTest <- univarDeSpearTest$orderframe$IDI[!(rownames(univarDeSpearTest$orderframe) %in% colnames(testDataFrame))]
testIDI$HCAS <- HCAS
testIDI$HCAS_T <- HCASTest
diffIDI$HCAS <-  HCASTest-HCAS

points(HCAS,HCASTest-HCAS,pch=3,col=colors[3])

DHCAP <- univarDeDri$orderframe$IDI[!(rownames(univarDeDriTest$orderframe) %in% colnames(testDataFrame))]
DHCAPTest <- univarDeDriTest$orderframe$IDI[!(rownames(univarDeDriTest$orderframe) %in% colnames(testDataFrame))]
testIDI$DHCAP <- DHCAP
testIDI$DHCAP_T <- DHCAPTest
diffIDI$DHCAP <-  DHCAPTest-DHCAP

points(DHCAP,DHCAPTest-DHCAP,pch=4,col=colors[4])

DHCAS <- univarDeDriSpear$orderframe$IDI[!(rownames(univarDeDriSpearTest$orderframe) %in% colnames(testDataFrame))]
DHCASTest <- univarDeDriSpearTest$orderframe$IDI[!(rownames(univarDeDriSpearTest$orderframe) %in% colnames(testDataFrame))]
testIDI$DHCAS <- DHCAS
testIDI$DHCAS_T <- DHCASTest
diffIDI$DHCAS <-  DHCASTest-DHCAS

points(DHCAS,DHCASTest-DHCAS,pch=5,col=colors[5])

PCA <- univarPCA$orderframe$IDI[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCATest <- univarPCATest$orderframe$IDI[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
testIDI$PCA <- PCA
testIDI$PCA_T <- PCATest
diffIDI$PCA <-  PCATest-PCA

points(PCA,PCATest-PCA,pch=6,col=colors[6])

EFA <- univarEFA$orderframe$IDI[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFATest <- univarEFATest$orderframe$IDI[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
testIDI$EFA <- EFA
testIDI$EFA_T <- EFATest
diffIDI$EFA <-  EFATest-EFA

points(EFA,EFATest-EFA,pch=7,col=colors[7])

WPCA <- univarWhite$orderframe$IDI[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCATest <- univarWhiteTest$orderframe$IDI[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
testIDI$WPCA <- WPCA
testIDI$WPCA_T <- WPCATest
diffIDI$WPCA <-  WPCATest-WPCA


points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])


names=c("Raw","HCA:P","HCA:S","DHCA:P","DHCA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)

legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)

2.11.8 Violin of differences


par(op)
par(mfrow=c(1,1),cex=0.7)

vioplot(diffIDI,
        ylim=c(-0.2,0.2),
        ylab="Test-Train",
        main="Test-Train Paired Predict IDI Difference",
        col=colors,
        cex.axis=0.6,
        las=2
)
stripchart(diffIDI, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffIDI),lapply(diffIDI,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.9 Distribution of Predict IDI in latent Variables

par(op)
par(mfrow=c(1,1),cex=0.7)

colors2 <- length(testIDI)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(testIDI,
        ylim=c(0.0,0.5),
        ylab="Predict IDI",
        main="Predict IDI of Latent Variables",
        col=colors2,
        cex.axis=0.6,
        las=2
)
stripchart(testIDI, method = "jitter", col = "gray",
           vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(testIDI),lapply(testIDI,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)

2.11.10 The tables


pander::pander(univarTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
ADAS13	20.942	6.12688	14.011	5.53542	0.297	0.243	0.781	0.794
RAVLT_immediate	28.621	7.92670	37.760	10.17622	0.459	0.144	0.689	0.765
ADAS11	12.972	4.60884	8.675	3.67173	0.105	0.196	0.755	0.764
M_ST40CV	0.179	0.00881	0.187	0.00735	0.929	0.178	0.739	0.758
M_ST29SV	0.125	0.00689	0.132	0.00734	0.837	0.173	0.736	0.751

pander::pander(univarDeTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
ADAS11	12.97235	4.60884	8.674712	3.67173	1.05e-01	0.1961	0.755	0.764
Hippocampus	0.15807	0.00857	0.166161	0.00928	7.63e-01	0.1643	0.733	0.738
FAQ	5.10606	4.92890	1.951923	2.93201	5.70e-12	0.1883	0.781	0.731
La_M_ST24TA	-0.00142	0.00291	0.000636	0.00278	2.98e-01	0.0684	0.617	0.703
M_ST31TA	0.01867	0.00189	0.019955	0.00166	5.36e-01	0.1171	0.704	0.699

pander::pander(univarDeSpearTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
ADAS11	12.9723	4.60884	8.6747	3.67173	1.05e-01	0.1961	0.755	0.764
M_ST24TA	0.0259	0.00426	0.0296	0.00378	6.49e-02	0.1182	0.684	0.743
FAQ	5.1061	4.92890	1.9519	2.93201	5.70e-12	0.1883	0.781	0.731
M_ST44TA	0.0206	0.00299	0.0228	0.00313	7.31e-01	0.0551	0.605	0.702
M_ST31TA	0.0187	0.00189	0.0200	0.00166	5.36e-01	0.1171	0.704	0.699

pander::pander(univarDeDriTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
ADAS13	20.9420	6.12688	14.011	5.53542	2.97e-01	0.243	0.781	0.794
M_ST29SV	0.1250	0.00689	0.132	0.00734	8.37e-01	0.173	0.736	0.751
FAQ	5.1061	4.92890	1.952	2.93201	5.70e-12	0.188	0.781	0.731
RAVLT_perc_forgetting	76.3100	29.68048	52.252	32.34899	5.27e-02	0.104	0.685	0.712
M_ST52TA	0.0178	0.00174	0.019	0.00160	5.12e-01	0.111	0.702	0.709

pander::pander(univarDeDriSpearTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
ADAS13	20.9420	6.12688	14.011	5.53542	2.97e-01	0.243	0.781	0.794
M_ST29SV	0.1250	0.00689	0.132	0.00734	8.37e-01	0.173	0.736	0.751
FAQ	5.1061	4.92890	1.952	2.93201	5.70e-12	0.188	0.781	0.731
RAVLT_perc_forgetting	76.3100	29.68048	52.252	32.34899	5.27e-02	0.104	0.685	0.712
M_ST52TA	0.0178	0.00174	0.019	0.00160	5.12e-01	0.111	0.702	0.709

pander::pander(univarPCATest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
RC5	-0.494	0.94917	0.314	0.90156	3.39e-01	0.1224	0.698	0.733
FAQ	5.106	4.92890	1.952	2.93201	5.70e-12	0.1883	0.781	0.731
RC1	-0.409	1.03506	0.260	0.88605	3.24e-01	0.0937	0.676	0.690
MMSE	27.000	1.74303	28.019	1.81768	2.43e-07	0.0770	0.672	0.668
M_ST53SV	0.143	0.00567	0.146	0.00676	2.99e-01	0.0381	0.598	0.634

pander::pander(univarEFATest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
MR6	-0.497	0.92084	0.316	0.85117	3.66e-01	0.1269	0.695	0.742
FAQ	5.106	4.92890	1.952	2.93201	5.70e-12	0.1883	0.781	0.731
MR1	-0.397	1.02251	0.252	0.90303	6.26e-01	0.0819	0.665	0.683
MMSE	27.000	1.74303	28.019	1.81768	2.43e-07	0.0770	0.672	0.668
M_ST53SV	0.143	0.00567	0.146	0.00676	2.99e-01	0.0381	0.598	0.634

pander::pander(univarWhiteTest$orderframe[1:TopVariables,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	IDI	ROCAUC	cStatCorr
L1	2.151	0.86834	1.386	0.96559	1.70e-01	0.1172	0.702	0.738
FAQ	5.106	4.92890	1.952	2.93201	5.70e-12	0.1883	0.781	0.731
L2	-2.238	0.97290	-2.909	0.92908	9.52e-01	0.0932	0.679	0.694
MMSE	27.000	1.74303	28.019	1.81768	2.43e-07	0.0770	0.672	0.668
M_ST53SV	0.143	0.00567	0.146	0.00676	2.99e-01	0.0381	0.598	0.634


topUni <- univar$orderframe$Name[1:TopVariables]
topDe <- univarDe$orderframe$Name[1:TopVariables]
topDeSpear <- univarDeSpear$orderframe$Name[1:TopVariables]
topDeDri <- univarDeDri$orderframe$Name[1:TopVariables]
topDeDriSpear <- univarDeDriSpear$orderframe$Name[1:TopVariables]
topPCA <- univarPCA$orderframe$Name[1:TopVariables]
topEFA <- univarEFA$orderframe$Name[1:TopVariables]
topPCAWhite <- univarWhite$orderframe$Name[1:TopVariables]

2.11.11 Model of top variables

par(mfrow=c(1,2),cex=0.6)

lmRAW <- glm(paste(outcome,"~."),
             trainDataFrame[,c(outcome,topUni)],
             family="binomial")
prRaw <- predictionStats_binary(cbind(testDataFrame[,outcome],predict(lmRAW,testDataFrame)),"Top Raw",cex=0.75)

Top Raw


lmDe <- glm(paste(outcome,"~."),
            DEdataframe[,c(outcome,topDe)],
            family="binomial")
prDe <- predictionStats_binary(cbind(predTestDe[,outcome],predict(lmDe,predTestDe)),"Top HCA:P",cex=0.75)

Top HCA:P


lmDeSpear <- glm(paste(outcome,"~."),
            DEdataframeSpear[,c(outcome,topDeSpear)],
            family="binomial")
prSpear <- predictionStats_binary(cbind(predTestDeSpear[,outcome],predict(lmDeSpear,predTestDeSpear)),"Top HCA:S",cex=0.75)

Top HCA:S


lmDri <- glm(paste(outcome,"~."),
            DriDEdataframe[,c(outcome,topDeDri)],
            family="binomial")
prDri <- predictionStats_binary(cbind(predTestDe[,outcome],predict(lmDri,predTestDri)),"Top DHCA:P",cex=0.75)

Top DHCA:P


lmDriSpear <- glm(paste(outcome,"~."),
            DriDEdataframeSpear[,c(outcome,topDeDriSpear)],
            family="binomial")
prDriSpear <- predictionStats_binary(cbind(predTestDriSpear[,outcome],predict(lmDriSpear,predTestDriSpear)),"Top DHCA:S",cex=0.7)

Top DHCA:S



lmPCA <- glm(paste(outcome,"~."),
            PCA_Train[,c(outcome,topPCA)],
            family="binomial")
prPCA <- predictionStats_binary(cbind(PCA_Predicted[,outcome],predict(lmPCA,PCA_Predicted)),"Top PCA",cex=0.75)

Top PCA



lmEFA <- glm(paste(outcome,"~."),
            EFA_Train[,c(outcome,topEFA)],
            family="binomial")
prEFA <- predictionStats_binary(cbind(EFA_Predicted[,outcome],predict(lmEFA,EFA_Predicted)),"Top EFA",cex=0.75)

Top EFA



lmPCAW <- glm(paste(outcome,"~."),
            PCAWhite_Train[,c(outcome,topPCAWhite)],
            family="binomial")
prWPCA <- predictionStats_binary(cbind(PCAWhitePredicted[,outcome],predict(lmPCAW,PCAWhitePredicted)),"Top White:PCA",cex=0.75)

Top White:PCA

par(op)

2.11.12 The Performance Tables and Plots


par(cex=0.6)

 aucs <- prRaw$aucs
  aucs <- rbind(aucs,prDe$aucs)
  aucs <- rbind(aucs,prSpear$aucs)
  aucs <- rbind(aucs,prDri$aucs)
  aucs <- rbind(aucs,prDriSpear$aucs)
  aucs <- rbind(aucs,prPCA$aucs)
  aucs <- rbind(aucs,prEFA$aucs)
  aucs <- rbind(aucs,prWPCA$aucs)

  
  rownames(aucs) <- c("RAW",
                        "HCA:P",
                        "HCA:S",
                        "DHCA:P",
                        "DHCA:S",
                        "PCA",
                        "EFA",
                        "WPCA"
                        )
  
  pander::pander(aucs)

	est	lower	upper
RAW	0.816	0.769	0.863
HCA:P	0.841	0.798	0.885
HCA:S	0.828	0.782	0.874
DHCA:P	0.844	0.800	0.887
DHCA:S	0.844	0.800	0.887
PCA	0.815	0.767	0.862
EFA	0.803	0.754	0.852
WPCA	0.816	0.769	0.863

  
  bpAUC <- barPlotCiError(as.matrix(aucs),
                          metricname = "ROC AUC",
                          thesets = "Test AUC",
                          themethod = rownames(aucs),
                          main = "ROC AUC",
                          offsets = c(0.5,1),
                          scoreDirection = ">",
                          ho=0.5,
                          args.legend = list(bg = "white",x="bottomleft",inset=c(0.0,0),cex=0.5),
                          col = terrain.colors(nrow(aucs))
                          )


  
 berror <- prRaw$berror
  berror <- rbind(berror,prDe$berror)
  berror <- rbind(berror,prSpear$berror)
  berror <- rbind(berror,prDri$berror)
  berror <- rbind(berror,prDriSpear$berror)
  berror <- rbind(berror,prPCA$berror)
  berror <- rbind(berror,prEFA$berror)
  berror <- rbind(berror,prWPCA$berror)


  rownames(berror) <- rownames(aucs)
  pander::pander(berror)

	50%	2.5%	97.5%
RAW	0.270	0.215	0.321
HCA:P	0.235	0.184	0.282
HCA:S	0.259	0.208	0.306
DHCA:P	0.241	0.187	0.293
DHCA:S	0.241	0.195	0.289
PCA	0.262	0.212	0.314
EFA	0.306	0.254	0.360
WPCA	0.274	0.224	0.328


  bpBER <- barPlotCiError(as.matrix(berror),
                          metricname = "Balanced Error Rate",
                          thesets = "Test BER",
                          themethod = rownames(aucs),
                          main = "Balanced Error Rate",
                          offsets = c(0.5,1),
                          scoreDirection = "<",
                          ho=0.5,
                          args.legend = list(bg = "white",x="topleft",inset=c(0.0,0),cex=0.5),
                          col = terrain.colors(nrow(aucs))
                          )

  par(op)

PCA EFA: TADPOLE

Jose Tamez

2023-02-07

1 TADPOLE Univariate

1.0.1 Loading the libraries

1.1 The data set

1.2 Conditioning the data

1.3 Get the Time To Event on MCI Subjects

2 Prognosis MCI to AD Conversion

2.1 the set

2.1.0.1 Standarize the names for the reporting

2.1.1 Data specs

2.2 Univariate

2.3 Decorrelation with UPSTM Blind

2.4 Decorrelation with UPSTM Blind/Spearman

2.5 Decorrelation with UPSTM Driven

2.6 Decorrelation with UPSTM Driven and Spearman

2.6.1 Get continous correlated features

2.7 PCA Analysis

2.8 EFA

2.9 White

2.10 Correlation Matrices

2.10.1 RAW

2.10.2 UPSTM Blind

2.10.3 UPSTM Blind/Spearman

2.10.4 UPSTM Driven

2.10.5 UPSTM Spearman

2.10.6 PCA

2.10.7 EFA

2.10.8 PCA Whitening

2.11 The Density Plot

2.11.1 Differences between train and test ROC AUC

2.11.2 Violin of differences

2.11.3 Distribution of ROC AUC in latent Variables

2.11.4 Differences between train and test Balanced Accuracy

2.11.5 Violin of differences

2.11.6 Distribution of Balanced Acc in latent Variables

2.11.7 Differences between train and test IDI

2.11.8 Violin of differences

2.11.9 Distribution of Predict IDI in latent Variables

2.11.10 The tables

2.11.11 Model of top variables

2.11.12 The Performance Tables and Plots